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Found 10 papers, 6 papers with code
Leveraging Model Fusion for Improved License Plate Recognition
License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement.
Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition
This work draws attention to the large fraction of near-duplicates in the training and test sets of datasets widely adopted in License Plate Recognition (LPR) research.
Measuring economic activity from space: a case study using flying airplanes and COVID-19
This work introduces a novel solution to measure economic activity through remote sensing for a wide range of spatial areas.
Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study
Active fire detection in satellite imagery is of critical importance to the management of environmental conservation policies, supporting decision-making and law enforcement.
Measuring Human and Economic Activity from Satellite Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic
We also show results on real examples of different sites before and after the COVID-19 outbreak to illustrate different measurable indicators.
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate.
Fine-Grained Vehicle Classification
License Plate Detection
+3
OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features
We compare the performance of state-of-the-art ConvNet architectures, and one specifically designed for sentiment analysis.
A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Cameras
We describe in this paper a Two-Stream Siamese Neural Network for vehicle re-identification.
Resource-Constrained Simultaneous Detection and Labeling of Objects in High-Resolution Satellite Images
We describe a strategy for detection and classification of man-made objects in large high-resolution satellite photos under computational resource constraints.
Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification
With this framework, we were able to reduce the training time while maintaining the classification performance of the ensemble.
